TY - GEN

T1 - Incremental map refinement of building information using lidar point clouds

AU - Zou, Qianqian

AU - Sester, Monika

N1 - Funding Information: This work was funded by the German Research Foundation (DFG) as a part of the Research Training Group GRK2159, “Integrity and collaboration in dynamic sensor networks” (i.c.sens).

PY - 2021/6/28

Y1 - 2021/6/28

N2 - For autonomous systems, an accurate and precise map of the environment is of importance. Mapping from LiDAR point clouds is one of the promising ways to generate 3D environment models. However, there are many problems caused by inaccurate data, missing areas, low density of points and sensor noise. Also, it is often not possible or accurate enough to generate a map from only one measurement campaign. In this paper, we propose a method to incrementally refine the map by several measurements from different campaigns and represent the map in a hierarchical way with a measure indicating uncertainty and the level of detail for objects. The idea is thus to store all captured information with a tentative semantics and uncertainty – even when it is not yet complete. Hence, occulated areas are presented as well, which can be possibly improved by the supplemental observation from the next measurement campaign. The proposed 3D environment model framework and the incremental update method are evaluated using LiDAR scans obtained from Riegl Mobile Mapping System.

AB - For autonomous systems, an accurate and precise map of the environment is of importance. Mapping from LiDAR point clouds is one of the promising ways to generate 3D environment models. However, there are many problems caused by inaccurate data, missing areas, low density of points and sensor noise. Also, it is often not possible or accurate enough to generate a map from only one measurement campaign. In this paper, we propose a method to incrementally refine the map by several measurements from different campaigns and represent the map in a hierarchical way with a measure indicating uncertainty and the level of detail for objects. The idea is thus to store all captured information with a tentative semantics and uncertainty – even when it is not yet complete. Hence, occulated areas are presented as well, which can be possibly improved by the supplemental observation from the next measurement campaign. The proposed 3D environment model framework and the incremental update method are evaluated using LiDAR scans obtained from Riegl Mobile Mapping System.

KW - 3D Quality Map

KW - Building Modelling

KW - Incremental Refinement

KW - LiDAR

KW - Mobile Mapping

KW - Point Cloud

UR - http://www.scopus.com/inward/record.url?scp=85116050542&partnerID=8YFLogxK

U2 - 10.5194/isprs-archives-xliii-b2-2021-277-2021

DO - 10.5194/isprs-archives-xliii-b2-2021-277-2021

M3 - Conference contribution

T3 - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

SP - 277

EP - 282

BT - International Archives of the Photogrammetry, Remote Sensing und Spatial Information Sciences

PB - Copernicus Publications

ER -